Blood Relatives: Splicing Mechanisms underlying Erythropoiesis in Health and Disease
Bibliographic record
Abstract
During erythropoiesis, hematopoietic stem and progenitor cells transition to erythroblasts en route to terminal differentiation into enucleated red blood cells. Transcriptome-wide changes underlie distinct morphological and functional characteristics at each cell division during this process. Many studies of gene expression have historically been carried out in erythroblasts, and the biogenesis of β-globin mRNA-the most highly expressed transcript in erythroblasts-was the focus of many seminal studies on the mechanisms of pre-mRNA splicing. We now understand that pre-mRNA splicing plays an important role in shaping the transcriptome of developing erythroblasts. Recent advances have provided insight into the role of alternative splicing and intron retention as important regulatory mechanisms of erythropoiesis. However, dysregulation of splicing during erythropoiesis is also a cause of several hematological diseases, including β-thalassemia and myelodysplastic syndromes. With a growing understanding of the role that splicing plays in these diseases, we are well poised to develop gene-editing treatments. In this review, we focus on changes in the developing erythroblast transcriptome caused by alternative splicing, the molecular basis of splicing-related blood diseases, and therapeutic advances in disease treatment using CRISPR/Cas9 gene editing.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".